Determining host preferences for accommodation listings

ABSTRACT

Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.

TECHNICAL FIELD

This invention relates to accommodation booking systems and inparticular to determining host preferences when accepting reservationrequests for an accommodation listing.

BACKGROUND

Online booking systems match guests looking for short term accommodationwith hosts offering accommodations. Hosts typically have certainpreferences that impact whether a particular request for anaccommodation is accepted or rejected by them. Such preferences may beexplicitly provided by the hosts, such as an indication that no pets areallowed, or that the accommodation is only available on weekends. Mostoften, however, the preferences are implicit and dependent upon severalvariables that are not easily quantifiable. Therefore, it can bedifficult to determine whether a particular request for a reservationwill be accepted or rejected

SUMMARY

An online booking system models the preferences of hosts based onstatistical relationships between features of previously receivedreservation requests and the acceptance of those reservation requests bythe hosts. In particular, the system classifies reservation requestsbased on a set of features. The preference of a host for a particularrequest feature is modeled based on the relationship between thereservation requests that possess the feature and the reservationrequests that are accepted by the host. The system uses the preferencemodel for a given listing to determine a probability that a prospectivereservation request for the listing will be accepted by the host. Theprobability may be used to generate accurate search results by loweringthe presentation rank of or filtering out search results where theprospective reservation request has a low probability of being acceptedby the host.

In some cases, sufficient historical data needed to generate an accuratepreference model is not available. To address this insufficiency ofdata, the preference model is generated based on historical data for agiven listing in combination with historical data for other listings inthe same region.

The features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram of the online booking system in accordancewith one embodiment.

FIG. 2 is a block diagram illustrating the different modules and storesincluded in the online booking system in accordance with one embodiment.

FIG. 3 is an illustration of the class diagram of the online bookingsystem in accordance with one embodiment.

FIG. 4 is a detailed illustration of the host preference module 229configured to generate a listing-specific preference model based onregional data in accordance with one embodiment.

FIG. 5 is a flowchart of an exemplary method for generating alisting-specific reservation request preference model based on regionaldata in accordance with one embodiment.

FIG. 6A is a flowchart of an exemplary method for generating a localpreference model for a listing based on data associated with the listingin accordance with one embodiment.

FIG. 6B is a flowchart of an exemplary method for generating a globalpreference model based on global data in accordance with one embodiment.

FIG. 7 is a flowchart of an exemplary method for generating searchresults for a search query based on preference models in accordance withone embodiment.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION System Overview

FIG. 1 is a system diagram of the online booking system in accordancewith one embodiment. FIG. 1 and the other figures use like referencenumerals to identify like elements. A letter after a reference numeral,such as “113A,” indicates that the text refers specifically to theelement having that particular reference numeral. A reference numeral inthe text without a following letter, such as “113,” refers to any or allof the elements in the figures bearing that reference numeral (e.g.“113” in the text refers to reference numerals “113A” and/or “113B” inthe figures).

The network 105 represents the communication pathways between users 103(e.g., consumers) and the online booking system 111. In one embodiment,the network is the Internet. The network can also utilize dedicated orprivate communication links (e.g. wide area networks (WANs),metropolitan area networks (MANs), or local area networks (LANs)) thatare not necessarily part of the Internet. The network uses standardcommunications technologies and/or protocols.

The client devices 101 are used by the users 103 for interacting withthe online booking system 111. A client device 101 can be any devicethat is or incorporates a computer such as a personal computer (PC), adesktop computer, a laptop computer, a notebook, a smartphone, or thelike. A computer is a device having one or more general or specialpurpose processors, memory, storage, and networking components (eitherwired or wireless). The client device 101 executes an operating system,for example, a Microsoft Windows-compatible operating system (OS), AppleOS X or iOS, a Linux distribution, or Google's Android OS. In someembodiments, the client device 101 may use a web browser 113, such asMicrosoft Internet Explorer, Mozilla Firefox, Google Chrome, AppleSafari and/or Opera, as an interface to interact with the online bookingsystem 111. In other embodiments, the client device 101 may execute adedicated application for accessing the online booking system 111.

The online booking system 111 includes web server 109 that presents webpages or other web content that form the basic interface visible to theusers 103. Users 103 use respective client devices 101 to access one ormore web pages, and provide data to the online booking system 111 viathe interface.

The online booking system 111 may be, for example, an online bookingsystem, a dining reservation system, a rideshare reservation system, aretail system, and the like. More generally, the online booking system111 provides users with access to an inventory of resources (e.g. goodsand services) that are available to consumers. The real world, physicallocation of each resource is considered as a factor in the consumer'sdecision to consume (e.g., purchase, rent, or otherwise obtain) theresource. Other factors include the listing type, the identity of thelisting owner, and reviews of users' who have previously used theservice offered by the listing.

In some embodiments, the online booking system 111 facilitatestransactions between users 103. For example, an online booking systemallows users 103 to book accommodations provided by other users of theonline booking system. A rideshare reservation system allows users 103to book rides from one location to another. An online marketplace systemallows users 103 to buy and/or sell goods or services face to face withother users. The online booking system 111 comprises additionalcomponents and modules that are described below.

Online Booking System

Referring to FIG. 2 and FIG. 3, in one embodiment the online bookingsystem 111 comprises a guest store 201, a host store 203, a listingstore 205, reservation request store 213, a booking store 207, a messagestore 209, a calendar 211, a booking module 215, a search module 217, anacceptance module 221, an availability module 223, and a messagingmodule 227. Those of skill in the art will appreciate that the onlinebooking system 111 may contain other modules that are not describedherein. In addition, conventional elements, such as firewalls,authentication systems, payment processing systems, network managementtools, load balancers, and so forth are not shown as they are notmaterial to the invention.

The system 111 may be implemented using a single computer, or a networkof computers, including cloud-based computer implementations. Thecomputers are preferably server class computers including one or morehigh-performance CPUs and 1G or more of main memory, and running anoperating system such as LINUX or variants thereof. The operations ofthe system 111 as described herein can be controlled through eitherhardware or through computer programs installed in non-transitorycomputer storage and executed by the processors to perform the functionsdescribed herein. The various stores (e.g., guest store 201, host store203, etc.) are implemented using non-transitory computer readablestorage devices, and suitable database management systems for dataaccess and retrieval. The system 111 includes other hardware elementsnecessary for the operations described here, including networkinterfaces and protocols, input devices for data entry, and outputdevices for display, printing, or other presentations of data.

The guest store 201 persistently stores data describing users (i.e.,guests) that inquire about listings for accommodation in the onlinebooking system 111, and is one means for performing this function. Eachguest is represented by a guest object 301, which may also be called aguest profile. The guest object 301 stores a unique identifierassociated with a given guest and information about the guest. Theinformation may include personal information such as name, user name,email address, location, phone number, gender, date of birth, personaldescription, education, work, reviews from other users, pictures, andthe like. The information may also include a guest score 311 and anexperienced flag 315. The guest score 311 provides a numericalrepresentation of the user's previous behavior as a guest. In someembodiments, the guest score is based on the scores (e.g., ratings)assigned by hosts from the guest's previous bookings. The experiencedflag 315 shows whether the guest is a frequent user of the onlinebooking system 111, and may be based, for example, on the total numberof times a guest has booked an accommodation through the online bookingsystem 111, the number of times a guest has used the online bookingsystem 111 in the recent past (e.g., number of accommodations a guesthas booked in the past 60 days), the length of time the guest has usedthe reservation system 111, or a combination of thereof.

The host store 203 persistently stores data describing users, herein“hosts,” that provided or are willing to provide accommodation to otherusers of the online booking system 111, and is one means for performingthis function. Each host is represented by host object 303, which mayalso be called a host profile. Information about hosts include personalinformation such as name, user name, email address, location, phonenumber, gender, date of birth, personal description, education, work,reviews from other users, pictures, and the like. Furthermore, the hoststore 203 may store additional information such as host score 331,outstanding messages 333, past guests 335, number of declines 337, andtime length 339. Each host object 303 is associated with one or morelistings 305, and with one or more guest objects 301. Each host isassigned a unique host ID.

The host score 331 provides a numerical representation of the user'sprevious behavior as a host. The host score can be based on the ratingassigned by guests from the host's previous bookings. Generally, eachtime a guest who books an accommodation from a host can provide a ratingof the host as well as the accommodation. The ratings are thenaggregated into a host score. The ratings can be weighted according tothe guests' own scores 311, as well as decayed based on the age of therating (i.e., how old the rating is).

A host can also use the online booking system 111 to requestaccommodations from other hosts, hence becoming a guest. In this casethe user will have a profile entry in both the guest store 201 and thehost store 203. Embodiments of the online booking system 111 may combinethe guest store 201 and the host store 203 into a single user profilestore. The user profile store will then store the personal informationas well as any guest related information and host related information ifapplicable. This scheme reduces the amount of redundant informationbetween the guest store 201 and the host store 203 when a user utilizesthe online booking system to both offer accommodation and reservationrequest accommodation.

The online booking system 111 enables, via the messaging module 227,guests and hosts to send messages to each other regardingaccommodations. Outstanding messages 333 counts the number of messagesfrom guests that the host has not responded to (i.e., number of messageswaiting for a response). Outstanding messages 333 measures theresponsiveness of a host to guests' reservation requests.

Past guests 335 counts the number of guests the host has accommodated.One embodiment counts the total number of guests a host has accommodatedsince the host started using the online booking system 111. Anotherembodiment only considers the number of guests a host has accommodatedin the recent past (e.g., in the past 30 days).

Number of declines 337 counts the number of times the host has rejecteda reservation request from a potential guest. A host may decline areservation request for any number of reasons. For example, areservation request from a guest for a particular accommodation may nothave met been for a sufficient number of nights; or the accommodationwas not in fact available and the host did not updated the listing'scalendar, as further described below.

Time length 339 measures the amount of time the host has been offeringaccommodation through the online booking system 111.

The listing store 205 stores information about the accommodationsoffered by hosts through listings, and is one means for performing thisfunction. Each offering of a given accommodation is represented bylisting object 305. Information about a listing includes the location351, price 353, unit type 355, amenities 357, and calendar 359. Thelisting store 205 may contain additional information such as a shortdescription of the accommodation, a list of house rules, photographs,etc. Each listing 305 is assigned a unique listing ID. Each listing 305is associated with a single host object 303.

Location 351 identifies the geographic location of the accommodation,such as the complete address, neighborhood, city, and/or country of theaccommodation offered.

Price 353 is the amount of money a guest needs to pay in order to obtainthe listed accommodation. The price 353 may be specified as an amount ofmoney per day, per week, per day, per month, and/or per season, or otherperiod of time specified by the host. Additionally price 353 may includeadditional charges such as cleaning fee, pet fee, and service fees.

Unit type 355 describes the type of accommodation being offered by thehost. Embodiments classify unit type into two groups, room type andproperty type. Types of room include entire home or apartment, privateroom, and shared room. Types of property include apartment, house, bed &breakfast, cabin, villa, castle, dorm, treehouse, boat, plane, parkingspace, car, van, camper or recreational vehicle, igloo, lighthouse,yurt, tipi, cave, island, chalet, earth house, hut, train, tent, loft,and the like.

Amenities 357 list the additional features that an accommodation offers.Amenities include smoking allowed, pets allowed, TV, cable TV, internet,wireless internet, air conditioning, heating, elevator, handicapaccessible, pool, kitchen, free parking on premise, doorman, gym, hottub, indoor fireplace, buzzer or wireless intercom, breakfast, family orkid friendly, suitable for events, washer, drier, and the like.

The host calendar 359 stores the availability of the accommodation foreach date in a date period, as specified by the host or determinedautomatically (e.g., through a calendar import process). That is, a hostaccesses the host calendar 359 for a listing, and manually indicateswhich dates that the listing is or is not available. The host calendar359 also includes information about the dates that the accommodation isunavailable because it has already been booked by a guest. In addition,the host calendar 359 continues to store historical information as tothe availability of the accommodation. Further, the host calendar 359may include calendar rules, e.g., the minimum and maximum number ofnights allowed.

The reservation request store 213 stores reservation requests made byguests, and is one means for performing this function. Each reservationrequest is represented by reservation request object 307. Informationstored about reservation requests include reservation request date 371,check-in date 373, number of nights 375, day of the week for check in377, day of the week for check out 379, holidays 381, and number ofguests 383. Each reservation request 307 is assigned a uniquereservation request ID. A given reservation request 307 is associatedwith an individual guest 301 and listing 305.

Reservation request date 371 specifies the date the reservation requestwas made. Check-in date 373 is the first day that accommodation isneeded by the inquiring guest. Number of nights 375 specifies the numberof nights the accommodation is needed by the guest. Check in day 377 andcheck out day 379 specify the day of the week (i.e. Monday, Tuesday,Wednesday, etc) that check in or check out is required. This informationdoes not need to be provided by the guest since it can be inferred fromthe check-in date 373 and the number of nights 375. This information isimportant since some hosts prefer or allow for only check in and/orcheck out in specific days of the week (e.g., only on weekdays or onlyon weekends). Holidays 381 indicates the dates (if any) of holidayswithin the inquired accommodation period. Number of guests 383stipulates the total number of people that are staying in theaccommodation.

Reservation requests 307 can be accepted or rejected by the host 303 ofthe listing 305 with which the reservation request 307 is associated,and the accepted flag 385 indicates whether the reservation request 307was accepted or rejected by the host 303. Reservation requests 307 canalso expire if they are not accepted by the host 303 of the listing 305within a threshold amount of time. In some embodiments the expirationtime of reservation requests 307 is set by the online booking system 111(e.g., reservation requests 307 expire if they are not accepted within24 hours since the time the reservation request 307 was submitted). Inother embodiments the expiration time of reservation requests 307 can bespecified by the host 303 or the guest 301. In yet other embodiments,reservation requests 307 may expire if they are not accepted a thresholdamount of time prior to the date 373 the accommodation was inquired for(e.g., reservation requests 307 may expire if they are not accepted twodays before the day the check-in date 373).

The message store 209 stores all the communications between hosts 103and guests 101, as exchanged via the messages module 227, and is onemeans for performing this function. Each message is associated with aguest 101, a host 103 and a listing 107. Guest may contact one or morehosts to obtain more information about their respective listings. Someguests may also use messages as a means to obtain more information aboutthe hosts and vise versa.

Additionally, the online booking system 111 may assign scores to hostsand guests based on their responsiveness to incoming messages. Everyhost and guest may be assigned a response rate score based on thepercentage of messages they reply to. Also users may be assigned aresponse time score based on the time average time it takes for them torespond to incoming messages.

The master calendar 211 stores information indicating the availabilityof every listing in the listings store 205, and is one means forperforming this function. Each host is responsible for updating thelisting calendar 359 for every listing they post in the online bookingsystem 111. This information is used to form the master calendar 211.

The search module 217 receives an input query from a guest and returns alist of accommodation listings that best match the input query, and isone means for performing this function. The search query includes searchparameters regarding the guest's trip, such as location (e.g., postalcode, city name, and country), check in date, check out date, number ofguests, and the like; and the guest's accommodations preferences, suchas room type, price range, amenities, and the like. The search modulethen retrieves all the listings that match the search query. In oneembodiment, Boolean matching is used for parameters such as location anddate, room type and price range, with additional parameters used tofurther filter the results.

In some embodiments the search module 217 ranks the returned searchresults based on a ranking score. The ranking score is a function of anumber of factors, such as price, host rating, distance from preferredlocation, listing, or a combination thereof. The ranking function can beimplemented as a linear combination or a multiplicative combination ofthe individual factors, where each factor is represented as a scaledvariable indicating a degree of match (e.g., 1 of an exact match of theunderlying search parameter, 0.5 for a partial or near match), andweighted with a weight to reflect the importance of the factor.Typically, location and date are highly weighted, and amenities arelesser weighted, but the particular weights are a design decision forthe system administrator. In one embodiment, the ranking factors includeinformation provided by the availability module 223 and the acceptancemodule 221, as further described below.

The acceptance module 221 calculates the probability of acceptance (PC)of a reservation request by a particular guest for a particular listingby the host, and is one means for performing this function. Embodimentsof the acceptance module 221 use an acceptance model for each host,based on reservation requests for listings that the host has acceptedand rejected historically. For given listing that satisfies a usersearch, the acceptance module 221 calculates the probability that if areservation request for the given listing is made by that guest, thehost will accept that reservation request. The search module 217 can usethe probability of acceptance as a ranking factor, when ranking thesearch results or to filter listings with a probability of acceptancelower than a threshold score. Generally, the search module 217 ranksmore highly listings that have a high probability of being accepted bythe host, and ranks more lowly listings that have a low probability ofbeing accepted.

The booking module 215 allows guests 301 to reserve an accommodationoffered through a listing 305, and is one means for performing thisfunction. In operation, the booking module 215 receives a reservationrequest 307 from a guest 301 to reserve an accommodation offered by aparticular listing 305. The reservation request includes reservationparameters, much like the search parameters in a search query, includingcheck in date, check out date, and the number of guests. The bookingmodule 215 presents the reservation request including the reservationparameters to the host 303 associated with the listing 305, and the host303 may either accept or reject the reservation request. If the host 303accepts the reservation request, then the booking module 215 updates theaccepted flag 485 to indicate that the reservation request was acceptedand also updates the booking store 207 and flags the booked days for thelisting as unavailable upon a host accepting a guest's reservationrequest. The booking store 207 stores information about all the acceptedand subsequently booked accommodation reservation requests. Each entryin the booking store 207 is associated with a host 303, a guest 301 anda listing 305.

Modeling Host Preferences

For a given listing 305, patterns may emerge with regards to the typesof reservation requests 307 that are accepted by the host 303 and thetypes of reservation requests 307 that are rejected by the host 303associated with the listing 305. The host preference module 229 modelsthe preferences of a host 303 of a particular listing 305 based on theacceptance patterns of reservation requests for the listing 305, and isone means for performing this function. The preference model is listingspecific. Therefore, a different preference model is generated for eachlisting associated with a given host in the online booking system 111.For the ease of discussion, a preference model for a given host andlisting combination is referred to hereinafter as a listing-specificpreference model. The host preference module 229 uses a listing-specificpreference model to estimate the probability that a new reservationrequest 307 for the listing 305 will be accepted by the host 303. Inother embodiments, the preference model is host specific, and covers allof the listings offered by a given host; thus there is one preferencemodel per host.

Data Used to Generate Preference Model

To generate a listing-specific preference model, the host preferencemodule 229 identifies a set of request features for classifyingreservation requests. In one embodiment, a request feature is binary—areservation request either possesses the feature or does not possess thefeature. Request features may be of many different types. Specifically,some request features may be related to the reservation request itself.Examples of such request features include whether the reservationrequest is for a given number or range of nights, whether thereservation request is for a given number or range of guests, whetherthe number of nights specified by the reservation request equals themaximum number of nights specified for a listing, whether the number ofguests equals the maximum/minimum number of guests specified for alisting, whether the requested accommodation falls on a holiday or aweekend, whether the gap between the check-in date and a previouscheck-out date of a different reservation is above/below a threshold orwithin a range, and the day/time of day of the reservation request. Inother embodiments, a request feature can be represented by a weightvalue that indicates the degree to which the reservation request has thefeature, to allow for partial or fuzzy matching. In another embodiment,a request feature is a numeric variable representing, for example, thenumber of nights.

Further, some request features may be related to the guest from whom thereservation request was received or the host associated with thelisting. Examples of request features related to the guest includewhether the guest is experienced, whether the score of the guest isabove/below a threshold, whether the guest has been verified, andwhether the guest has a picture associated with his/her profile.Examples of request features related to the host include whether thehost was previously contacted by the guest, whether the host replied tothe guest, and whether the host has outstanding messages. Personsskilled in the art will recognize that other request features notexplicitly listed above are within the scope here.

The host preference module 229 generates a feature vector for eachreservation request based on the identified set of request features andthe historical data stored in the guest store 201, host store 203,listing store 205, message store 209, and reservation request store 213.For a given reservation request, the feature vector includes a binaryvalue for each request feature, forming a bit vector. If the reservationrequest possesses the feature, then the binary value indicates a “1” ora “true.” Conversely, if the reservation request does not possess thefeature, then the binary value indicates a “0” or a “false.” For theremainder of this discussion, a request feature is also referred to asf_(x), where each x denotes a type of the request feature. For a givenreservation request, then, the feature vector takes the form of: [f_(a),f_(b), f_(c), . . . , f_(n)], where the binary value for each of f_(a),f_(b), f_(c), . . . , and f_(n) depends on whether the reservationrequest possesses the feature. The feature vector also includes anacceptance value that indicates whether the reservation request for thelisting was accepted or rejected by the host. For the remainder of thisdiscussion, the acceptance value feature is also referred to as A. Asnoted, in another embodiment, the request features can be represented byweights, to form a vector of real numbers (e.g., values between 0 and1), or a combination of binary and real numbers.

Often there is insufficient historical data available to create anaccurate listing-specific preference model. To address thisinsufficiency of data, two different approaches may be taken by the hostpreference module 229. In the first approach, the historical data for afirst listing is augmented with historical data for other listingshaving one or more common attributes with the first listing (referred toherein as the “cluster approach”). FIG. 4 and FIG. 5 and thecorresponding description include details of the cluster approach. Inthe second approach, historical data for all listings is used instead ofdata specific to the listing or the region (referred to herein as the“individual approach”). FIG. 6 and the corresponding description includedetails of the individual approach. With both approaches, the preferencemodel that is generated allows the host preference module 229 toestimate the probability that a future reservation request for thelisting will be accepted by a host. FIG. 7 and the correspondingdescription include details of using a preference model to improvesearch results presented to a prospective guest.

Cluster Approach to Generating a Preference Model

In the cluster approach, the host preference module 229 generates thelisting-specific preference model based on a cluster of reservationrequests. The cluster includes reservation requests for the listing aswell as reservation requests for other listings having one or moreattributes in common with the listing. The attribute may be the listingsbeing in the same geographic region, having the same type (e.g., roomtype), having the same type of host (e.g., property manager), or may bebased on collaborative filtering. Training the model based on clusterdata, as opposed to data associated with only the listing, enables thehost preference module 229 to generate an accurate model when the dataassociated with the listing is insufficient.

The following discussion describes the process for generating alisting-specific preference model for a cluster of reservation requestsfor listings in the same region. Persons skilled in the art wouldreadily understand that the described process may be used to generate alisting-specific preference model for a cluster of reservation requestsfor listings having one or more different common attributes, such ashaving the same listing type or host type.

FIG. 4 is a detailed illustration of the host preference module 229configured to generate a listing-specific preference model based oncluster data in accordance with one embodiment. As shown, the hostpreference module 229 includes a cluster feature vector generator 401, apreference vector generator 403, and a preference relevance generator405. The functional details of these components are discussed below withreference to generating a listing-specific preference model for listingL located in region G and hosted by host H.

The cluster feature vector generator 401 (also “generator 401”)generates a cluster feature vector for a collection of previouslyreceived reservation requests for listings included in a particularcluster, and is one means for performing this function. In operation,for listing L, the generator 401 identifies the geographic region Gwithin which the listing L is located. A geographic region may be aneighborhood, zip code, city, county, a country, or any arbitrarygeographic area determined by the host preference module 229. Thegenerator 401 identifies a collection of previously received reservationrequests for listing L as well as reservation requests for otherlistings in region G. In one embodiment, reservation requests for only asubset of all the other listings in region G are selected. The subset oflistings may be selected arbitrarily or based on a similarity betweenlisting L and the subset of the other listings. For example, the subsetmay be those listings having the same unit type as listing L and withinregion G. Further, only a subset of reservation requests for a givenlisting may be selected for the set of reservation requests. Forexample, only those reservation requests made within a particular timeperiod, such as the last six months, may be selected.

The generator 401 generates a feature vector for each reservationrequest in the set of reservation requests. As discussed above, for agiven set of request features, a feature vector identifies whether thereservation request possesses each of the request features. The manydifferent types of request features are listed above, but let's assumethat the feature vectors generated by the generator 401 include fourfeatures f_(a), f_(b), f_(c), and f_(d), and the acceptance value A. Inthis example, the value off, indicates whether the requestedaccommodation fell on a weekend/holiday. The value of f_(b) indicateswhether the requested accommodation was for the maximum number of nightsoffered for the listing. The value of f_(c) indicates whether thecheck-in date for the requested accommodation was at least 1 full dayafter the check-out date for a previous booking. The value of f_(d)indicates whether the host had previously been contacted by the guest.The value of A indicates whether the host accepted or rejected thereservation request.

For each feature vector, the generator 401 accesses the relevantstore(s) in the online booking system 111 that store data enabling thegenerator 401 to determine whether the associated reservation requestpossesses each request feature. For example, for f_(a), the generator401 accesses the reservation request store 312 to determine thespecifics dates for which the reservation request was made. If the datesfell on a weekend/holiday, then f_(a) is set to “1.” If the dates didnot fall on a weekend/holiday, then f_(a) is set to “0.” Similarly, forf_(b), the generator 401 accesses the reservation request store 312 todetermine the number of nights for which the reservation request wasmade. The generator 401 also accesses the listing store 205 to determinethe maximum number of nights specified for the listing. In the numbersmatch, then f_(b) is set to “1.” If the numbers do not match, then f_(b)is set to “0.” In such a manner, the generator 401 accesses the data inthe different stores to fill in the feature vector for each of the setof reservation requests.

The generator 401 then combines the individual feature vectors for theset of reservation requests to generate a cluster feature vector thatrepresents the set of reservation requests. For example, if there arethree listings including listing L in the region G, then the featurevectors of reservation requests for the three listings are combined togenerate the cluster feature vector. Assume that, in such an example,five reservation requests have been made for the three listings. Thecluster feature vector may then take the form of:

$\begin{pmatrix}f_{a} & f_{b} & f_{c} & f_{d} & A \\1 & 0 & 1 & 1 & 0 \\0 & 0 & 0 & 1 & 0 \\1 & 0 & 0 & 1 & 1 \\1 & 1 & 1 & 0 & 1 \\0 & 1 & 1 & 1 & 0\end{pmatrix}\quad$

where each column of the vector corresponds to one of f_(a), f_(b),f_(c), f_(d), and A, and each row corresponds to a different reservationrequest for one of the three listings. In this example, the first tworows may correspond to reservation request 1 and reservation request 2for listing L, rows three and four may correspond to reservation request1 and reservation request 2 for listing M in region G, and the last rowmay correspond to reservation request 1 for listing N in region G.

The preference vector generator 403 (also “generator 403”) processes thecluster feature vector to generate a listing-specific preference vector,and is one means for performing this function. The listing-specificpreference vector identifies a preference value of a host of aparticular listing for each request feature. The preference vector takesthe form of: [β_(la), β_(lb), β_(lc), . . . , β_(ln)].

To compute the listing-specific preference vector, the generator 403first computes a cluster preference value for each request feature inthe cluster feature vector. A cluster preference value for a givenrequest feature combines the preferences of all listings in the cluster,e.g., listings in the same region, by computing the average or median ofthe preference across all listings or reservation requests representedby the cluster feature vector. In one embodiment, a cluster preferencevalue is computed using the following formula:

$\beta_{x} = \frac{\sum\limits_{i = 1}^{T}\; {f_{x} \times A}}{\sum\limits_{i = 1}^{T}\; f_{x}}$

where β_(x) is the cluster preference value for a given request feature,T is the total number of reservation requests represented by the clusterfeature vector, f_(c) is the value of the request feature for a givenreservation request represented by the cluster feature vector, and A isthe value that indicates whether or not the reservation request wasaccepted by the host.

Continuing the example above with reservation requests for listings L-N,the cluster preference value β_(a) for request feature f_(a) can becomputed by evaluating the following equation. When evaluated, the valueof β_(a) is ˜0.67.

$\beta_{a} = \frac{\sum\limits_{i = 1}^{5}\; {f_{a} \times A}}{\sum\limits_{i = 1}^{5}\; f_{x}}$

Once the cluster preference values are computed for each of the requestfeatures, the generator 403 can compute the listing-specific preferencevector for the particular listing. Again, the listing-specificpreference vector identifies a preference value of a host of aparticular listing for each request feature. In operation, the generator403 computes a listing-specific preference value for each requestfeature based on a combination of the cluster preference value and alocal preference value for the feature. The local preference value for agiven request feature indicates the median or mean preference for therequest feature across all reservation requests for the particularlisting. In one embodiment, a local preference value is computed usingthe following formula:

$\beta_{lx} = \frac{{\sum\limits_{i = 1}^{Y_{l}}\; {f_{x} \times A}} + {k\; \beta_{x}}}{{\sum\limits_{i = 1}^{Y_{l}}\; f_{x}} + k}$

where β_(lx) is the listing-specific preference value for a givenrequest feature, Y_(l) is the total number of reservation requestsrepresented in the cluster feature vector for the listing l, f_(x) isthe value of the request feature for a given reservation requestrepresented in the cluster feature vector, A is indicating whether thereservation request was accepted or rejected by the host, and k is aweight to be assigned to the cluster preference value for the requestfeature.

Continuing the example above with reservation requests for listings L-N,the listing-specific preference value β_(la) for request feature f_(a)can be computed by evaluating the following equation. In the equationbelow, the weight k is set to 0.5. Any other suitable weight is withinthe scope. When evaluated, the value of β_(la) is ˜0.13.

$\beta_{la} = \frac{{\sum\limits_{i = 1}^{2}\; {f_{a} \times A}} + {0.5 \times 0.67}}{2 + 0.5}$

As discussed above, the listing-specific preference values [β_(la),β_(lb), β_(lc), . . . , β_(ld)] form the listing-specific preferencevector. The preference model generator 405 (also the “generator 405”)generates the listing-specific preference model for a listing based onthe listing-specific preference vector, and is one means for performingthis function. To generate the model, the generator 405 first generatesthe training data set for the model. The training data set is generatedby applying the listing-specific preference vector to the featurevectors of the reservation requests for the listing represented by thecluster feature vector. Specifically, for a given feature vector, thevalue of each request feature is multiplied by the listing-specificpreference value for that request feature to generate a preferencescore. The collection of resulting feature vectors form the trainingdata set for the model.

The generator 405 next estimates based on the training data set theeffect of each preference on whether a host ultimately accepts orrejects a reservation request. The estimated effect of each preferenceis a parameter of the preference model, and the collection of theparameters form the preference model. In one embodiment, the generator405 computes the parameters of the preference model using a logisticregression that identifies the statistical relationship between theacceptance or rejection of a reservation request as represented by A andthe request features. Specifically, the generator 405 sets up anequation associated with each reservation request represented by thecluster feature vector. Each equation takes the form of:

A=θ _(o)+[θ_(a)×β_(la) ·f _(a)]+[θ_(b)×β_(lb) ·f _(b)]+[θ_(c)×β_(lc) ·f_(c)]+[θ_(d)×β_(ld) ·f _(a)]

where A is the value indicating whether the reservation request wasaccepted or rejected, θ_(o) is a constant parameter of the parametermodel, θ_(a) is a parameter of the parameter model that indicates thestatistical relationship between f_(a) and A, β_(la)·f_(a) is thepreference score for request feature f_(a) in the training data set,θ_(b) is a parameter of the parameter model that indicates thestatistical relationship between f_(b) and A, β_(lb)·f_(b) is thepreference score for request feature f_(b) in the training data set,θ_(c) is a parameter of the parameter model that indicates thestatistical relationship between f_(c) and A, β_(lc)·f_(c) is thepreference score for request feature f_(c) in the training data set, andθ_(d) is a parameter of the parameter model that indicates thestatistical relationship between f_(d) and A, β_(ld)·f_(d) is thepreference score for request feature f_(d) in the training data set.

The generator 405 then processes the equations to estimate values forthe parameters: θ_(o), θ_(a), θ_(b), θ_(c), and θ_(d). Collectively, theparameters form the preference model associated with the collection oflistings included in the region. The preference model can be used toestimate the probability that a future reservation request for thelisting will be accepted by the host of the listing. As discussed indetail in conjunction with FIG. 7 below, such a probability can be usedto rank and filter search results for listings.

FIG. 5 is a flowchart of an exemplary method for generating alisting-specific reservation request preference model based on regionaldata in accordance with one embodiment. The method in FIG. 5 isdescribed in the context of generating the preference model for listingL. The method begins with the host preference module 229 identifying 501a set of reservation requests for listings, including listing L, in thegeographic area in which listing L is located. Each reservation requesthas previously been accepted or rejected by the host of the listing.

The host preference module 229 generates 503 a cluster feature vectorbased on features of the identified set of reservation requests.Specifically, the host preference module 229 first generates a featurevector for each of the set of reservation requests. Each feature vectorindicating whether or not the reservation request possesses each of aset of request features. The feature vectors for the set of reservationrequests collectively form the cluster feature vector.

The host preference module 229 generates 505 a listing-specificpreference vector for listing L based on the generated cluster featurevector. The listing-specific preference vector includes a preferencevalue of the host of listing L for each request feature. To generate thelisting-specific preference vector, the host preference module 229 firstcomputes a regional median or mean preference for each request featureacross all listings represented by the cluster feature vector. Theregional median or mean preference is combined with the host'spreference for the request feature to compute the preference value ofthe host of listing L for the request feature.

Based on the listing-specific preference vector, the host preferencemodule 229 generates 507 the preference model for the region to whichlisting L belongs. The preference model indicates the statisticalrelationship between the acceptance of a reservation request and eachrequest feature. To determine the model, the generator 405 firstgenerates the training data set for the model. The training data set isgenerated by applying the listing-specific preference vector to thefeature vectors of the reservation requests for listings in the region.The host preference module 229 then estimates based on the training dataset the statistical relationship between the acceptance of a reservationrequest, each request feature, and the host/listing preferences. Theestimated statistical relationship is a parameter of the preferencemodel, and the collection of the parameters form the preference model.

The host preference module 229 stores 509 the preference model in themodel store 231 in association with the region to which the listingsbelong. The preference model may be updated periodically based on dataassociated with new reservation requests for listing L and/or the otherlistings in the region.

Individual Approach to Generating a Preference Model

In the individual approach, the host preference module 229 generates twopreference models: (i) a local preference model generated based onreservation requests for the listing, and (ii) a global preference modelgenerated based on reservation requests across all listings available inthe online booking system 111. FIG. 6A describes the process forgenerating the local preference model, and FIG. 6B describes the processfor generating the global preference model. When the data associatedwith the listing is sufficient to generate an accurate model, the localpreference model is used to predict the probability of a futurereservation request for the listing being accepted. When the dataassociated with the listing is insufficient, the global preference modelis used to predict the probability for the future reservation requestbeing accepted.

FIG. 6A is a flowchart of an exemplary method for generating a localpreference model for a listing based on data associated with the listingin accordance with one embodiment. The method in FIG. 6A is described inthe context of generating the preference model for listing L. The methodbegins with the host preference module 229 identifying 501 a set ofreservation requests for the listing L. Each reservation request haspreviously been accepted or rejected by the host of the listing L.

The host preference module 229 generates 603 a listing-specific featurevector based on features of the identified set of reservation requests.The listing-specific feature vector includes a vector for each of theset of reservation requests. For a given reservation request, a featurevector identifies whether the reservation request possesses each of aset of request features. Let's assume that the feature vectors generatedby the generator 401 include four features f_(a), f_(b), f_(c), andf_(d), and the acceptance value A. The host preference module 229combines the individual feature vectors for the set of reservationrequests to generate the listing-specific feature vector that representsthe set of reservation requests. For example, assume that fivereservation requests have been made for listing L. The listing-specificfeature vector for listing L may then take the form of:

$\begin{pmatrix}f_{a} & f_{b} & f_{c} & f_{d} & A \\0 & 0 & 0 & 1 & 1 \\1 & 0 & 1 & 0 & 0 \\1 & 0 & 0 & 1 & 1 \\1 & 0 & 1 & 0 & 1 \\0 & 1 & 1 & 1 & 0\end{pmatrix}\quad$

where each column of the vector corresponds to one of f_(a), f_(b),f_(c), f_(d), and A, and each row corresponds to a different reservationrequest for listing L.

Based on the listing-specific feature vector, the host preference module229 generates 607 a local preference model for listing L. The localpreference model indicates the statistical relationship between theacceptance of a reservation request for listing L and each requestfeature. The training data set for the local preference model isgenerated based on the listing-specific feature vector. The hostpreference module 229 estimates based on the training data set thestatistical relationship between the acceptance of a reservation requestand each request feature. The estimated statistical relationship of eachrequest feature is a parameter of the preference model, and thecollection of the parameters form the preference model.

In one embodiment, the host preference module 229 computes theparameters of the local preference model using a logistic regressionthat identifies the statistical relationship between the acceptance orrejection of a reservation request as represented by A and the requestfeatures. Specifically, the host preference module 229 sets up a set ofequations, each equation associated with a different reservation requestrepresented by the listing-specific feature vector for listing L. Eachequation takes the form of:

A=γ _(o)+[γ_(a) ×f _(a)]+[γ_(b) ×f _(b)]+[γ_(c) ×f _(c)]+[γ_(d) ×f _(d)]

where A is the value indicating whether the reservation request wasaccepted or rejected, γ_(o) is a constant parameter of the parametermodel, γ_(a) is a parameter of the parameter model that indicates thestatistical relationship between f_(a) and A, f_(a) is the value forrequest feature f_(a) in the listing-specific feature vector, γ_(b) is aparameter of the parameter model that indicates the statisticalrelationship between f_(b) and A, f_(b) is the value for request featuref_(b) in the listing-specific feature vector, γ_(c) is a parameter ofthe parameter model that indicates the statistical relationship betweenf_(c) and A, f_(c) is the value for request feature f_(c) in thelisting-specific feature vector, and γ_(d) is a parameter of theparameter model that indicates the statistical relationship betweenf_(d) and A, f_(d) is the value for request feature f_(d) in thelisting-specific feature vector. The host preference module 229 thenprocesses the equations to estimate values for the parameters: γ_(o),γ_(a), γ_(b), γ_(c), and γ_(d). Collectively, the parameters form thelocal preference model associated with listing L. The local preferencemodel can be used to estimate the probability that a future reservationrequest for the listing will be accepted by the host of the listing.

The host preference module 229 stores 607 the local preference model inthe listing store 205 in association with listing L. The localpreference model may be updated periodically based on data associatedwith new reservation requests for listing L.

FIG. 6B is a flowchart of an exemplary method for generating a globalpreference model based on global data in accordance with one embodiment.The method begins with the host preference module 229 identifying 609 aset of reservation requests associated with all the listings in theonline booking system 111. Each reservation request has previously beenaccepted or rejected by the host of the listing.

The host preference module 229 generates 611 a global feature vectorbased on features of the identified set of reservation requests. Theglobal feature vector includes a vector for each of the set ofreservation requests. For a given reservation request, a feature vectoridentifies whether the reservation request possesses each of a set ofrequest features. Let's assume that the feature vectors generated by thegenerator 401 include four features f_(a), f_(b), f_(c), and f_(d), andthe acceptance value A. The host preference module 229 combines theindividual feature vectors for the set of reservation requests togenerate the global feature vector that represents the set ofreservation requests.

Based on the global feature vector, the host preference module 229generates 613 a global preference model. The global preference modelindicates the statistical relationship between the acceptance of areservation request for any listing and each request feature. Thetraining data set for the global preference model is generated based onthe global feature vector which includes feature vectors for allreservation requests across all listings. The host preference module 229estimates based on the training data set the statistical relationshipbetween the acceptance of a reservation request and each requestfeature. The estimated statistical relationship of each request featureis a parameter of the global preference model, and the collection of theparameters form the global preference model.

In one embodiment, the host preference module 229 computes theparameters of the local preference model using a logistic regressionthat identifies the statistical relationship between the acceptance orrejection of a reservation request as represented by A and the requestfeatures. The specifics of the logistic regression in this embodimentare the same as those discussed above with the local preference model.

The host preference module 229 stores 615 the global preference model inthe listing store 205. The global preference model may be updatedperiodically based on data associated with new reservation requests forthe listings in the online booking system 111.

Improving Search Results with Preference Models

Preference models generated by the host preference module 229 may beused the search module 217 to improve the search results presented to aprospective guest in response to receiving a search query. Specifically,a preference model may be used by the search module 217 to determine aprobability that a reservation request for a listing associated with asearch result would be accepted by the host of the listing. The searchmodule 217 may then rank the search results according to the determinedprobabilities and/or filter certain search results having a determinedprobability below a threshold.

FIG. 7 is a flowchart of an exemplary method for generating searchresults for a search query based on preference models in accordance withone embodiment. The method beings with the search module 217 receiving701 a search query from a guest. A search query typically includesseveral parameters that define the scope of the query. Parameters mayinclude the geographic region, unit type, check-in and check-out dates,and the number of guests. In response to the search query, the searchmodule 217 identifies 703 a set of listings in the listing store 205that satisfy the search parameters.

For each listing, the search module 217 computes 705 based on apreference model associated with the listing the probability that aprospective reservation request for the listing, matching the parametersof the search query, from the guest would be accepted by the host of thelisting. The computation of the probability differs depending on whetherthe host preference module 229 is following the cluster approach or theindividual approach.

In the case of the cluster approach, the search module 217 selects thepreference model stored in the preference module store 231. The searchmodule 217 then identifies the parameters specified in the preferencemodel and applies those parameters derived from the search query to theparameters derived from the search query. Specifically, the searchmodule 217 breaks the search query into request features and applies therelevant parameter to each request feature. In one embodiment, thefollowing equation is used to apply the parameters to the prospectivereservation request:

L=θ _(o)+[θ_(a)×β_(la) ·f _(a)]+[θ_(b)×β_(lb) ·f _(b)]+[θ_(c)β_(lc) ·f_(c)]+[θ_(a)×β_(ld) ·f _(d)]

where L is the value indicates the likelihood that the prospectivereservation request will be accepted, θ_(o) is a constant parameter ofthe parameter model, θ_(a) is a parameter of the preference model thatindicates the statistical relationship between f_(a) and acceptance ofthe reservation request, β_(la) is the listing-specific preference forrequest feature f_(a), f_(a) is the value for a request feature of theprospective reservation request, θ_(b) indicates the statisticalrelationship between a given host preference β_(lb) and the acceptanceof the reservation request, β_(lb) is the listing-specific preferencefor request feature f_(b), f_(b) is the value for request feature forthe prospective reservation request, θ_(c) is a parameter of thepreference model that indicates the statistical relationship betweenf_(c) and acceptance of the reservation request, β_(lc) is thelisting-specific preference for request feature f_(c), f_(c) is thevalue for request feature for the prospective reservation request, θ_(d)is a parameter of the preference model that indicates the statisticalrelationship between f_(d) and acceptance of the reservation request,β_(ld) is the listing-specific preference for request feature f_(d), andf_(d) is the value for request feature for the prospective reservationrequest.

In one embodiment, L from the equation above, i.e., the likelihood thatthe prospective reservation request will be accepted, is transformedinto a probability using the following logistic formula:

$\frac{1}{( {1 + ^{- L}} )}$

In the case of the host preference module 229 following the individualapproach, the search module 217 selects between the global preferencemodule and the local preference module stored in association with thelisting. The search module 217 makes the selection based on the numberof historical reservation requests received for the listing based onwhich the local preference module was generated. When the number ofreservation requests is below a threshold, then the search module 217applies the parameters of the global preference model to the prospectivereservation request. When the number of reservation requests is above athreshold, the search module 217 applies the parameters of the localpreference model to the prospective reservation request. The mechanismfor applying the global preference model or the local preference modelto the prospective reservation request is the same as that discussedabove with respect to the cluster approach. In one embodiment, when thenumber of reservation requests is above a threshold, the search module217 computes a global probability based on the global preference modeland a local probability based on the local preference model. The twoprobabilities are then combined to generate the probability of theprospective reservation request being accepted.

Once the probability of a reservation request being accepted is computedfor the identified listings, the search module 217 presents 707 thelistings to the prospective guest based on the computed probabilities.In one embodiment, the search module 217 ranks the listings based on thecomputed probabilities. Other features, such as the quality of thelistings, the price, etc., may further influence the rankings of thelistings. In another embodiment, the search module 217 filters listingsfrom being presented that have computed probabilities below a threshold.

ALTERNATIVE APPLICATIONS

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter.

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A computer implemented method, comprising:identifying a plurality of listings having at least one commonattribute, each listing associated with a host; determining a set ofreservation requests received for the plurality of listings, eachreservation request for a listing having previously been accepted orrejected by the host associated with the listing; determining a clusterpreference value for a request feature based on a number of the set ofreservation requests that were accepted and a number of the set ofreservation requests that possess the request feature; for a firstlisting in the plurality of listings, determining based on the clusterpreference value a listing-specific preference value for the requestfeature; generating, using a computing device, a preference model basedon the listing-specific preference value, the preference modelidentifying a relationship between the request feature and a reservationrequest for the first listing being accepted or rejected by a first hostassociated with the first listing; and applying the preference model toa prospective reservation request for the first listing to compute aprobability that the prospective reservation request will be accepted bythe first host.
 2. The method of claim 1, wherein the request featureindicates a mechanism for classifying the set of reservation requests.3. The method of claim 1, wherein determining the listing-specificpreference value comprises: identifying a subset of the reservationrequests that were received for the first listing; and determining anumber of the subset that were accepted and a number of the subset thatpossess the request feature; determining the listing-specific valuebased on a combination of the cluster preference value, the number ofthe subset that were accepted and the number of the subset that possessthe request feature.
 4. The method of claim 1, wherein generating thepreference model comprises generating a training data set by applyingthe listing-specific value to a subset of the reservation requests thatwere received for the first listing.
 5. The method of claim 4, furthercomprising processing the training data set using a logistic regressionto determine the relationship between the request feature and areservation request for the first listing being accepted or rejected bya first host associated with the first listing.
 6. The method of claim1, further comprising: determining that the prospective reservationrequest is associated with a search query received from a prospectiveguest; determining whether to display the search result to theprospective guest based on the computed probability.
 7. The method ofclaim 6, wherein determining whether to display the search resultcomprises comparing the computed probability with an acceptancethreshold, and displaying the search result with the computedprobability is equal to or above the acceptance threshold.
 8. The methodof claim 1, wherein the first listing is for an accommodation beingoffered for reservation by the first host, and the request feature is agap feature indicating a specific period of time between a previousreservation or calendar unavailability of the accommodation ending and areservation associated with a reservation request beginning.
 9. Acomputer implemented method, comprising: determining a set ofreservation requests received for a listing, each reservation requestfor the listing having previously been accepted or rejected by a hostassociated with the listing; determining a preference value for arequest feature based on a number of the set of reservation requeststhat were accepted and a number of the set of reservation requests thatpossess the request feature; and generating, using a computing device, apreference model based on the preference value, the preference modelidentifying a relationship between the request feature and a reservationrequest for the first listing being accepted or rejected by a first hostassociated with the first listing.
 10. The method of claim 9, furthercomprising: determining a set of global reservation requests receivedfor all listings, each reservation request having previously beenaccepted or rejected by a host; determining a global preference valuefor a request feature based on a number of the set of global reservationrequests that were accepted and a number of the set of globalreservation requests that possess the request feature; and generating,using a computing device, a global preference model based on the globalpreference value, the preference model identifying a relationshipbetween the request feature and a reservation request for the firstlisting being accepted or rejected by a host.
 11. The method of claim10, further comprising: when the set of reservation requests is above orequal to a threshold number, applying the preference model to aprospective reservation request for the first listing to compute aprobability that the prospective reservation request will be accepted bythe first host; or when the set of reservation requests is below athreshold number, applying the global preference model to theprospective reservation request to compute the probability.
 12. Anon-transitory computer readable medium storing instructions, theinstructions when executed cause the processor to: identify a pluralityof listings having at least one common attribute, each listingassociated with a host; determine a set of reservation requests receivedfor the plurality of listings, each reservation request for a listinghaving previously been accepted or rejected by the host associated withthe listing; determine a cluster preference value for a request featurebased on a number of the set of reservation requests that were acceptedand a number of the set of reservation requests that possess the requestfeature; for a first listing in the plurality of listings, determinebased on the cluster preference value a listing-specific preferencevalue for the request feature; generate, using a computing device, apreference model based on the listing-specific preference value, thepreference model identifying a relationship between the request featureand a reservation request for the first listing being accepted orrejected by a first host associated with the first listing; and applythe preference model to a prospective reservation request for the firstlisting to compute a probability that the prospective reservationrequest will be accepted by the first host.
 13. The non-transitorycomputer readable medium of claim 12, wherein the request featureindicates a mechanism for classifying the set of reservation requests.14. The non-transitory computer readable medium of claim 12, whereindetermining the listing-specific preference value comprises: identifyinga subset of the reservation requests that were received for the firstlisting; and determining a number of the subset that were accepted and anumber of the subset that possess the request feature; determining thelisting-specific value based on a combination of the cluster preferencevalue, the number of the subset that were accepted and the number of thesubset that possess the request feature.
 15. The non-transitory computerreadable medium of claim 12, wherein generating the preference modelcomprises generating a training data set by applying thelisting-specific value to a subset of the reservation requests that werereceived for the first listing.
 16. The non-transitory computer readablemedium of claim 15, further comprising processing the training data setusing a logistic regression to determine the relationship between therequest feature and a reservation request for the first listing beingaccepted or rejected by a first host associated with the first listing.17. The non-transitory computer readable medium of claim 12, furthercomprising: determining that the prospective reservation request isassociated with a search query received from a prospective guest;determining whether to display the search result to the prospectiveguest based on the computed probability.
 18. The non-transitory computerreadable medium of claim 17, wherein determining whether to display thesearch result comprises comparing the computed probability with anacceptance threshold, and displaying the search result with the computedprobability is equal to or above the acceptance threshold.
 19. Thenon-transitory computer readable medium of claim 12, wherein the firstlisting is for an accommodation being offered for reservation by thefirst host, and the request feature is a gap feature indicating aspecific period of time between a previous reservation or calendarunavailability of the accommodation ending and a reservation associatedwith a reservation request beginning.
 20. A computer system comprising:a processor; and a memory storing instructions that, when executed bythe processor, cause the processor to: identify a plurality of listingshaving at least one common attribute, each listing associated with ahost; determine a set of reservation requests received for the pluralityof listings, each reservation request for a listing having previouslybeen accepted or rejected by the host associated with the listing;determine a cluster preference value for a request feature based on anumber of the set of reservation requests that were accepted and anumber of the set of reservation requests that possess the requestfeature; for a first listing in the plurality of listings, determinebased on the cluster preference value a listing-specific preferencevalue for the request feature; generate, using a computing device, apreference model based on the listing-specific preference value, thepreference model identifying a relationship between the request featureand a reservation request for the first listing being accepted orrejected by a first host associated with the first listing; and applythe preference model to a prospective reservation request for the firstlisting to compute a probability that the prospective reservationrequest will be accepted by the first host.